Overview

Dataset statistics

Number of variables21
Number of observations8000
Missing cells91
Missing cells (%)0.1%
Total size in memory1.3 MiB
Average record size in memory168.0 B

Variable types

Numeric9
Text12

Alerts

id has unique valuesUnique

Reproduction

Analysis started2025-11-21 18:50:28.602132
Analysis finished2025-11-21 18:50:29.123010
Duration0.52 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct8000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4000.5
Minimum1
Maximum8000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-21T18:50:29.297636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile400.95
Q12000.75
median4000.5
Q36000.25
95-th percentile7600.05
Maximum8000
Range7999
Interquartile range (IQR)3999.5

Descriptive statistics

Standard deviation2309.54541
Coefficient of variation (CV)0.5773141882
Kurtosis-1.2
Mean4000.5
Median Absolute Deviation (MAD)2000
Skewness0
Sum32004000
Variance5334000
MonotonicityStrictly increasing
2025-11-21T18:50:29.412392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79841
 
< 0.1%
79831
 
< 0.1%
79821
 
< 0.1%
79811
 
< 0.1%
79801
 
< 0.1%
79791
 
< 0.1%
79781
 
< 0.1%
79771
 
< 0.1%
79761
 
< 0.1%
79751
 
< 0.1%
Other values (7990)7990
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
ValueCountFrequency (%)
80001
< 0.1%
79991
< 0.1%
79981
< 0.1%
79971
< 0.1%
79961
< 0.1%
Distinct970
Distinct (%)12.1%
Missing6
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:29.687291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.596572429
Min length1

Characters and Unicode

Total characters44739
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKeep
2nd rowOthers
3rd rowOn
4th rowChoose
5th rowMrs
ValueCountFrequency (%)
scene19
 
0.2%
seven17
 
0.2%
choose16
 
0.2%
goal15
 
0.2%
soldier15
 
0.2%
city15
 
0.2%
begin15
 
0.2%
everything15
 
0.2%
according15
 
0.2%
particularly15
 
0.2%
Other values (960)7837
98.0%
2025-11-21T18:50:30.030425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5589
 
12.5%
r3076
 
6.9%
t3049
 
6.8%
i2893
 
6.5%
o2847
 
6.4%
a2810
 
6.3%
n2760
 
6.2%
l1868
 
4.2%
s1557
 
3.5%
u1413
 
3.2%
Other values (40)16877
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)44739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5589
 
12.5%
r3076
 
6.9%
t3049
 
6.8%
i2893
 
6.5%
o2847
 
6.4%
a2810
 
6.3%
n2760
 
6.2%
l1868
 
4.2%
s1557
 
3.5%
u1413
 
3.2%
Other values (40)16877
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5589
 
12.5%
r3076
 
6.9%
t3049
 
6.8%
i2893
 
6.5%
o2847
 
6.4%
a2810
 
6.3%
n2760
 
6.2%
l1868
 
4.2%
s1557
 
3.5%
u1413
 
3.2%
Other values (40)16877
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5589
 
12.5%
r3076
 
6.9%
t3049
 
6.8%
i2893
 
6.5%
o2847
 
6.4%
a2810
 
6.3%
n2760
 
6.2%
l1868
 
4.2%
s1557
 
3.5%
u1413
 
3.2%
Other values (40)16877
37.7%
Distinct970
Distinct (%)12.1%
Missing8
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:30.287909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.565815816
Min length1

Characters and Unicode

Total characters44482
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSignificant
2nd rowLeast
3rd rowCulture
4th rowDescribe
5th rowRock
ValueCountFrequency (%)
listen21
 
0.3%
theory16
 
0.2%
friend16
 
0.2%
newspaper16
 
0.2%
mind16
 
0.2%
form16
 
0.2%
easy16
 
0.2%
quality16
 
0.2%
painting15
 
0.2%
phone15
 
0.2%
Other values (960)7829
98.0%
2025-11-21T18:50:30.637331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5564
 
12.5%
r3031
 
6.8%
t3000
 
6.7%
i2905
 
6.5%
o2890
 
6.5%
a2737
 
6.2%
n2734
 
6.1%
l1914
 
4.3%
s1603
 
3.6%
u1370
 
3.1%
Other values (40)16734
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)44482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5564
 
12.5%
r3031
 
6.8%
t3000
 
6.7%
i2905
 
6.5%
o2890
 
6.5%
a2737
 
6.2%
n2734
 
6.1%
l1914
 
4.3%
s1603
 
3.6%
u1370
 
3.1%
Other values (40)16734
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5564
 
12.5%
r3031
 
6.8%
t3000
 
6.7%
i2905
 
6.5%
o2890
 
6.5%
a2737
 
6.2%
n2734
 
6.1%
l1914
 
4.3%
s1603
 
3.6%
u1370
 
3.1%
Other values (40)16734
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5564
 
12.5%
r3031
 
6.8%
t3000
 
6.7%
i2905
 
6.5%
o2890
 
6.5%
a2737
 
6.2%
n2734
 
6.1%
l1914
 
4.3%
s1603
 
3.6%
u1370
 
3.1%
Other values (40)16734
37.6%

num_bathrooms
Real number (ℝ)

Distinct7964
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4985.33333
Minimum0.63
Maximum9999.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-21T18:50:30.735633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.63
5-th percentile490.345
Q12469.1825
median4970.02
Q37471.0675
95-th percentile9526.5685
Maximum9999.5
Range9998.87
Interquartile range (IQR)5001.885

Descriptive statistics

Standard deviation2891.461396
Coefficient of variation (CV)0.5799935942
Kurtosis-1.199378381
Mean4985.33333
Median Absolute Deviation (MAD)2501.005
Skewness0.007682976472
Sum39882666.64
Variance8360549.007
MonotonicityNot monotonic
2025-11-21T18:50:30.836214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6574.512
 
< 0.1%
1690.582
 
< 0.1%
1583.362
 
< 0.1%
6107.22
 
< 0.1%
7126.412
 
< 0.1%
3335.282
 
< 0.1%
9668.982
 
< 0.1%
6804.292
 
< 0.1%
8164.422
 
< 0.1%
2633.762
 
< 0.1%
Other values (7954)7980
99.8%
ValueCountFrequency (%)
0.631
< 0.1%
1.281
< 0.1%
2.031
< 0.1%
3.181
< 0.1%
3.541
< 0.1%
ValueCountFrequency (%)
9999.51
< 0.1%
9998.881
< 0.1%
9998.081
< 0.1%
9997.331
< 0.1%
9996.451
< 0.1%
Distinct970
Distinct (%)12.1%
Missing6
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:31.135167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.551038279
Min length1

Characters and Unicode

Total characters44375
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAction
2nd rowColor
3rd rowHear
4th rowServe
5th rowSize
ValueCountFrequency (%)
off18
 
0.2%
nation17
 
0.2%
natural17
 
0.2%
tend17
 
0.2%
collection16
 
0.2%
item16
 
0.2%
us16
 
0.2%
rock16
 
0.2%
billion16
 
0.2%
mind16
 
0.2%
Other values (960)7829
97.9%
2025-11-21T18:50:31.530688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5607
 
12.6%
r3050
 
6.9%
t2963
 
6.7%
o2926
 
6.6%
a2806
 
6.3%
i2796
 
6.3%
n2666
 
6.0%
l1960
 
4.4%
s1476
 
3.3%
u1359
 
3.1%
Other values (40)16766
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)44375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5607
 
12.6%
r3050
 
6.9%
t2963
 
6.7%
o2926
 
6.6%
a2806
 
6.3%
i2796
 
6.3%
n2666
 
6.0%
l1960
 
4.4%
s1476
 
3.3%
u1359
 
3.1%
Other values (40)16766
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5607
 
12.6%
r3050
 
6.9%
t2963
 
6.7%
o2926
 
6.6%
a2806
 
6.3%
i2796
 
6.3%
n2666
 
6.0%
l1960
 
4.4%
s1476
 
3.3%
u1359
 
3.1%
Other values (40)16766
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5607
 
12.6%
r3050
 
6.9%
t2963
 
6.7%
o2926
 
6.6%
a2806
 
6.3%
i2796
 
6.3%
n2666
 
6.0%
l1960
 
4.4%
s1476
 
3.3%
u1359
 
3.1%
Other values (40)16766
37.8%
Distinct969
Distinct (%)12.1%
Missing2
Missing (%)< 0.1%
Memory size62.6 KiB
2025-11-21T18:50:32.087316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.547636909
Min length1

Characters and Unicode

Total characters44370
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowTen
2nd rowHour
3rd rowSing
4th rowBank
5th rowJust
ValueCountFrequency (%)
kitchen18
 
0.2%
situation18
 
0.2%
on17
 
0.2%
sister17
 
0.2%
want17
 
0.2%
left16
 
0.2%
recently16
 
0.2%
wrong16
 
0.2%
follow15
 
0.2%
tax15
 
0.2%
Other values (959)7833
97.9%
2025-11-21T18:50:32.424188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5579
 
12.6%
r2987
 
6.7%
o2950
 
6.6%
t2939
 
6.6%
i2878
 
6.5%
a2788
 
6.3%
n2661
 
6.0%
l1928
 
4.3%
s1507
 
3.4%
u1351
 
3.0%
Other values (40)16802
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)44370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5579
 
12.6%
r2987
 
6.7%
o2950
 
6.6%
t2939
 
6.6%
i2878
 
6.5%
a2788
 
6.3%
n2661
 
6.0%
l1928
 
4.3%
s1507
 
3.4%
u1351
 
3.0%
Other values (40)16802
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5579
 
12.6%
r2987
 
6.7%
o2950
 
6.6%
t2939
 
6.6%
i2878
 
6.5%
a2788
 
6.3%
n2661
 
6.0%
l1928
 
4.3%
s1507
 
3.4%
u1351
 
3.0%
Other values (40)16802
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5579
 
12.6%
r2987
 
6.7%
o2950
 
6.6%
t2939
 
6.6%
i2878
 
6.5%
a2788
 
6.3%
n2661
 
6.0%
l1928
 
4.3%
s1507
 
3.4%
u1351
 
3.0%
Other values (40)16802
37.9%
Distinct970
Distinct (%)12.1%
Missing9
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:32.660762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.536603679
Min length1

Characters and Unicode

Total characters44243
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowThat
2nd rowCapital
3rd rowSignificant
4th rowCustomer
5th rowAbout
ValueCountFrequency (%)
discuss19
 
0.2%
give18
 
0.2%
continue17
 
0.2%
follow17
 
0.2%
cultural17
 
0.2%
present17
 
0.2%
consider17
 
0.2%
language17
 
0.2%
and17
 
0.2%
bit16
 
0.2%
Other values (960)7819
97.8%
2025-11-21T18:50:32.984869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5559
 
12.6%
t3015
 
6.8%
r2953
 
6.7%
o2898
 
6.6%
i2809
 
6.3%
n2718
 
6.1%
a2676
 
6.0%
l1928
 
4.4%
s1575
 
3.6%
u1391
 
3.1%
Other values (40)16721
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)44243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5559
 
12.6%
t3015
 
6.8%
r2953
 
6.7%
o2898
 
6.6%
i2809
 
6.3%
n2718
 
6.1%
a2676
 
6.0%
l1928
 
4.4%
s1575
 
3.6%
u1391
 
3.1%
Other values (40)16721
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5559
 
12.6%
t3015
 
6.8%
r2953
 
6.7%
o2898
 
6.6%
i2809
 
6.3%
n2718
 
6.1%
a2676
 
6.0%
l1928
 
4.4%
s1575
 
3.6%
u1391
 
3.1%
Other values (40)16721
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5559
 
12.6%
t3015
 
6.8%
r2953
 
6.7%
o2898
 
6.6%
i2809
 
6.3%
n2718
 
6.1%
a2676
 
6.0%
l1928
 
4.4%
s1575
 
3.6%
u1391
 
3.1%
Other values (40)16721
37.8%
Distinct969
Distinct (%)12.1%
Missing9
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:33.265130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.519584533
Min length1

Characters and Unicode

Total characters44107
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowSuggest
2nd rowTwo
3rd rowBenefit
4th rowThird
5th rowAccording
ValueCountFrequency (%)
contain19
 
0.2%
mouth18
 
0.2%
quickly17
 
0.2%
seem17
 
0.2%
it17
 
0.2%
street16
 
0.2%
billion16
 
0.2%
create16
 
0.2%
maybe16
 
0.2%
sort16
 
0.2%
Other values (959)7823
97.9%
2025-11-21T18:50:33.653372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5468
 
12.4%
r3094
 
7.0%
t3042
 
6.9%
o2930
 
6.6%
a2788
 
6.3%
i2767
 
6.3%
n2679
 
6.1%
l1871
 
4.2%
s1468
 
3.3%
u1403
 
3.2%
Other values (40)16597
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)44107
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5468
 
12.4%
r3094
 
7.0%
t3042
 
6.9%
o2930
 
6.6%
a2788
 
6.3%
i2767
 
6.3%
n2679
 
6.1%
l1871
 
4.2%
s1468
 
3.3%
u1403
 
3.2%
Other values (40)16597
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44107
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5468
 
12.4%
r3094
 
7.0%
t3042
 
6.9%
o2930
 
6.6%
a2788
 
6.3%
i2767
 
6.3%
n2679
 
6.1%
l1871
 
4.2%
s1468
 
3.3%
u1403
 
3.2%
Other values (40)16597
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44107
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5468
 
12.4%
r3094
 
7.0%
t3042
 
6.9%
o2930
 
6.6%
a2788
 
6.3%
i2767
 
6.3%
n2679
 
6.1%
l1871
 
4.2%
s1468
 
3.3%
u1403
 
3.2%
Other values (40)16597
37.6%

latitude
Real number (ℝ)

Distinct7981
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4993.906715
Minimum0.1
Maximum9997.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-21T18:50:33.756335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile486.283
Q12493.4625
median4937.705
Q37516.9075
95-th percentile9510.9175
Maximum9997.95
Range9997.85
Interquartile range (IQR)5023.445

Descriptive statistics

Standard deviation2889.856235
Coefficient of variation (CV)0.578676455
Kurtosis-1.192816345
Mean4993.906715
Median Absolute Deviation (MAD)2509.73
Skewness0.01303242669
Sum39951253.72
Variance8351269.057
MonotonicityNot monotonic
2025-11-21T18:50:33.861449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1684.612
 
< 0.1%
467.612
 
< 0.1%
3710.032
 
< 0.1%
9890.472
 
< 0.1%
1774.452
 
< 0.1%
8329.232
 
< 0.1%
4924.052
 
< 0.1%
2283.082
 
< 0.1%
7201.692
 
< 0.1%
7492.342
 
< 0.1%
Other values (7971)7980
99.8%
ValueCountFrequency (%)
0.11
< 0.1%
0.121
< 0.1%
1.791
< 0.1%
2.051
< 0.1%
2.971
< 0.1%
ValueCountFrequency (%)
9997.951
< 0.1%
9992.851
< 0.1%
9992.621
< 0.1%
9991.951
< 0.1%
9990.811
< 0.1%

longitude
Real number (ℝ)

Distinct7972
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4988.411745
Minimum0.23
Maximum9999.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-21T18:50:33.961699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile484.919
Q12506.6575
median4960.93
Q37519.395
95-th percentile9477.6875
Maximum9999.42
Range9999.19
Interquartile range (IQR)5012.7375

Descriptive statistics

Standard deviation2888.141296
Coefficient of variation (CV)0.5789701098
Kurtosis-1.201110667
Mean4988.411745
Median Absolute Deviation (MAD)2499.975
Skewness0.006951936833
Sum39907293.96
Variance8341360.144
MonotonicityNot monotonic
2025-11-21T18:50:34.066358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9533.782
 
< 0.1%
2770.462
 
< 0.1%
9117.252
 
< 0.1%
4723.582
 
< 0.1%
2729.922
 
< 0.1%
9627.882
 
< 0.1%
3220.592
 
< 0.1%
9097.322
 
< 0.1%
3970.212
 
< 0.1%
2633.362
 
< 0.1%
Other values (7962)7980
99.8%
ValueCountFrequency (%)
0.231
< 0.1%
1.661
< 0.1%
1.891
< 0.1%
2.251
< 0.1%
2.791
< 0.1%
ValueCountFrequency (%)
9999.421
< 0.1%
9998.881
< 0.1%
9998.871
< 0.1%
9996.581
< 0.1%
9994.891
< 0.1%
Distinct970
Distinct (%)12.1%
Missing7
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:34.406206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.509445765
Min length1

Characters and Unicode

Total characters44037
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowFire
2nd rowLead
3rd rowNice
4th rowGrowth
5th rowYard
ValueCountFrequency (%)
art20
 
0.3%
amount19
 
0.2%
of19
 
0.2%
claim18
 
0.2%
create18
 
0.2%
speech17
 
0.2%
major17
 
0.2%
south17
 
0.2%
often17
 
0.2%
ball17
 
0.2%
Other values (960)7814
97.8%
2025-11-21T18:50:34.872971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5435
 
12.3%
r3065
 
7.0%
t2988
 
6.8%
o2955
 
6.7%
a2802
 
6.4%
i2757
 
6.3%
n2660
 
6.0%
l1916
 
4.4%
s1581
 
3.6%
u1356
 
3.1%
Other values (40)16522
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)44037
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5435
 
12.3%
r3065
 
7.0%
t2988
 
6.8%
o2955
 
6.7%
a2802
 
6.4%
i2757
 
6.3%
n2660
 
6.0%
l1916
 
4.4%
s1581
 
3.6%
u1356
 
3.1%
Other values (40)16522
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44037
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5435
 
12.3%
r3065
 
7.0%
t2988
 
6.8%
o2955
 
6.7%
a2802
 
6.4%
i2757
 
6.3%
n2660
 
6.0%
l1916
 
4.4%
s1581
 
3.6%
u1356
 
3.1%
Other values (40)16522
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44037
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5435
 
12.3%
r3065
 
7.0%
t2988
 
6.8%
o2955
 
6.7%
a2802
 
6.4%
i2757
 
6.3%
n2660
 
6.0%
l1916
 
4.4%
s1581
 
3.6%
u1356
 
3.1%
Other values (40)16522
37.5%
Distinct969
Distinct (%)12.1%
Missing12
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:35.242014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.525663495
Min length1

Characters and Unicode

Total characters44139
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowTop
2nd rowEvery
3rd rowSimply
4th rowFund
5th rowRecord
ValueCountFrequency (%)
box18
 
0.2%
why17
 
0.2%
first16
 
0.2%
surface16
 
0.2%
language15
 
0.2%
health15
 
0.2%
exactly15
 
0.2%
speech15
 
0.2%
fear15
 
0.2%
official15
 
0.2%
Other values (959)7831
98.0%
2025-11-21T18:50:35.736806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5443
 
12.3%
t2999
 
6.8%
r2992
 
6.8%
a2880
 
6.5%
o2853
 
6.5%
i2787
 
6.3%
n2681
 
6.1%
l1932
 
4.4%
s1550
 
3.5%
c1361
 
3.1%
Other values (40)16661
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)44139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5443
 
12.3%
t2999
 
6.8%
r2992
 
6.8%
a2880
 
6.5%
o2853
 
6.5%
i2787
 
6.3%
n2681
 
6.1%
l1932
 
4.4%
s1550
 
3.5%
c1361
 
3.1%
Other values (40)16661
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5443
 
12.3%
t2999
 
6.8%
r2992
 
6.8%
a2880
 
6.5%
o2853
 
6.5%
i2787
 
6.3%
n2681
 
6.1%
l1932
 
4.4%
s1550
 
3.5%
c1361
 
3.1%
Other values (40)16661
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5443
 
12.3%
t2999
 
6.8%
r2992
 
6.8%
a2880
 
6.5%
o2853
 
6.5%
i2787
 
6.3%
n2681
 
6.1%
l1932
 
4.4%
s1550
 
3.5%
c1361
 
3.1%
Other values (40)16661
37.7%

average_guest_rating
Real number (ℝ)

Distinct7972
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4951.00396
Minimum0.75
Maximum9998.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-21T18:50:35.839179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile492.9855
Q12459.5375
median4880.25
Q37497
95-th percentile9502.471
Maximum9998.68
Range9997.93
Interquartile range (IQR)5037.4625

Descriptive statistics

Standard deviation2907.045944
Coefficient of variation (CV)0.5871629203
Kurtosis-1.214656662
Mean4951.00396
Median Absolute Deviation (MAD)2521.81
Skewness0.03409912932
Sum39608031.68
Variance8450916.119
MonotonicityNot monotonic
2025-11-21T18:50:35.947883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2743.653
 
< 0.1%
2386.662
 
< 0.1%
6488.322
 
< 0.1%
2783.532
 
< 0.1%
4371.632
 
< 0.1%
616.442
 
< 0.1%
9737.042
 
< 0.1%
3357.942
 
< 0.1%
423.422
 
< 0.1%
7343.582
 
< 0.1%
Other values (7962)7979
99.7%
ValueCountFrequency (%)
0.751
< 0.1%
1.631
< 0.1%
2.051
< 0.1%
5.581
< 0.1%
7.221
< 0.1%
ValueCountFrequency (%)
9998.681
< 0.1%
9998.571
< 0.1%
9998.151
< 0.1%
9997.931
< 0.1%
9996.591
< 0.1%
Distinct970
Distinct (%)12.1%
Missing7
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:36.241886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.532465908
Min length1

Characters and Unicode

Total characters44221
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome
2nd rowOr
3rd rowItself
4th rowAgent
5th rowTruth
ValueCountFrequency (%)
since20
 
0.3%
response18
 
0.2%
explain17
 
0.2%
everybody16
 
0.2%
quality16
 
0.2%
recent16
 
0.2%
return16
 
0.2%
goal16
 
0.2%
table15
 
0.2%
attack15
 
0.2%
Other values (960)7828
97.9%
2025-11-21T18:50:36.642599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5578
 
12.6%
r2999
 
6.8%
t2922
 
6.6%
o2833
 
6.4%
i2804
 
6.3%
a2755
 
6.2%
n2714
 
6.1%
l1943
 
4.4%
s1569
 
3.5%
u1407
 
3.2%
Other values (40)16697
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)44221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5578
 
12.6%
r2999
 
6.8%
t2922
 
6.6%
o2833
 
6.4%
i2804
 
6.3%
a2755
 
6.2%
n2714
 
6.1%
l1943
 
4.4%
s1569
 
3.5%
u1407
 
3.2%
Other values (40)16697
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5578
 
12.6%
r2999
 
6.8%
t2922
 
6.6%
o2833
 
6.4%
i2804
 
6.3%
a2755
 
6.2%
n2714
 
6.1%
l1943
 
4.4%
s1569
 
3.5%
u1407
 
3.2%
Other values (40)16697
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5578
 
12.6%
r2999
 
6.8%
t2922
 
6.6%
o2833
 
6.4%
i2804
 
6.3%
a2755
 
6.2%
n2714
 
6.1%
l1943
 
4.4%
s1569
 
3.5%
u1407
 
3.2%
Other values (40)16697
37.8%
Distinct7967
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4933.303201
Minimum0.77
Maximum9999.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-21T18:50:36.751776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.77
5-th percentile487.531
Q12437.8025
median4888.865
Q37418.0425
95-th percentile9459.369
Maximum9999.77
Range9999
Interquartile range (IQR)4980.24

Descriptive statistics

Standard deviation2875.267853
Coefficient of variation (CV)0.5828281246
Kurtosis-1.186947639
Mean4933.303201
Median Absolute Deviation (MAD)2496.215
Skewness0.0242650755
Sum39466425.61
Variance8267165.226
MonotonicityNot monotonic
2025-11-21T18:50:36.851383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6075.872
 
< 0.1%
3970.812
 
< 0.1%
8590.852
 
< 0.1%
39682
 
< 0.1%
246.92
 
< 0.1%
8847.362
 
< 0.1%
5737.342
 
< 0.1%
8075.612
 
< 0.1%
8678.112
 
< 0.1%
1988.682
 
< 0.1%
Other values (7957)7980
99.8%
ValueCountFrequency (%)
0.771
< 0.1%
1.291
< 0.1%
1.331
< 0.1%
2.291
< 0.1%
3.461
< 0.1%
ValueCountFrequency (%)
9999.771
< 0.1%
9999.581
< 0.1%
9999.091
< 0.1%
9996.451
< 0.1%
9994.221
< 0.1%
Distinct970
Distinct (%)12.1%
Missing9
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:37.115835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.552121136
Min length1

Characters and Unicode

Total characters44367
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWork
2nd rowProduct
3rd rowPay
4th rowLow
5th rowCommon
ValueCountFrequency (%)
tree19
 
0.2%
light17
 
0.2%
party16
 
0.2%
even16
 
0.2%
simple16
 
0.2%
stop16
 
0.2%
voice16
 
0.2%
compare15
 
0.2%
claim15
 
0.2%
as15
 
0.2%
Other values (960)7830
98.0%
2025-11-21T18:50:37.464618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5553
 
12.5%
r3047
 
6.9%
t2958
 
6.7%
o2902
 
6.5%
i2796
 
6.3%
a2778
 
6.3%
n2685
 
6.1%
l1900
 
4.3%
s1554
 
3.5%
u1412
 
3.2%
Other values (40)16782
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)44367
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5553
 
12.5%
r3047
 
6.9%
t2958
 
6.7%
o2902
 
6.5%
i2796
 
6.3%
a2778
 
6.3%
n2685
 
6.1%
l1900
 
4.3%
s1554
 
3.5%
u1412
 
3.2%
Other values (40)16782
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44367
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5553
 
12.5%
r3047
 
6.9%
t2958
 
6.7%
o2902
 
6.5%
i2796
 
6.3%
a2778
 
6.3%
n2685
 
6.1%
l1900
 
4.3%
s1554
 
3.5%
u1412
 
3.2%
Other values (40)16782
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44367
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5553
 
12.5%
r3047
 
6.9%
t2958
 
6.7%
o2902
 
6.5%
i2796
 
6.3%
a2778
 
6.3%
n2685
 
6.1%
l1900
 
4.3%
s1554
 
3.5%
u1412
 
3.2%
Other values (40)16782
37.8%

distance_to_city_center_km
Real number (ℝ)

Distinct7968
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5007.704513
Minimum0.31
Maximum9999.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-21T18:50:37.561474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.31
5-th percentile479.5025
Q12424.28
median5002.905
Q37550.9025
95-th percentile9509.7455
Maximum9999.76
Range9999.45
Interquartile range (IQR)5126.6225

Descriptive statistics

Standard deviation2907.252076
Coefficient of variation (CV)0.5805558353
Kurtosis-1.219257251
Mean5007.704513
Median Absolute Deviation (MAD)2563.03
Skewness0.000445921284
Sum40061636.1
Variance8452114.634
MonotonicityNot monotonic
2025-11-21T18:50:37.683733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8627.832
 
< 0.1%
4437.132
 
< 0.1%
9681.472
 
< 0.1%
7254.822
 
< 0.1%
9983.272
 
< 0.1%
4713.082
 
< 0.1%
8989.182
 
< 0.1%
8732.362
 
< 0.1%
1077.252
 
< 0.1%
1030.632
 
< 0.1%
Other values (7958)7980
99.8%
ValueCountFrequency (%)
0.311
< 0.1%
1.991
< 0.1%
4.391
< 0.1%
5.991
< 0.1%
6.381
< 0.1%
ValueCountFrequency (%)
9999.761
< 0.1%
9998.381
< 0.1%
9997.971
< 0.1%
9996.041
< 0.1%
9994.621
< 0.1%
Distinct970
Distinct (%)12.1%
Missing6
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:37.967358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.520140105
Min length1

Characters and Unicode

Total characters44128
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowDaughter
2nd rowContain
3rd rowMother
4th rowGroup
5th rowSpend
ValueCountFrequency (%)
receive21
 
0.3%
court18
 
0.2%
thus18
 
0.2%
source17
 
0.2%
national17
 
0.2%
effect16
 
0.2%
appear16
 
0.2%
brother16
 
0.2%
sport16
 
0.2%
society16
 
0.2%
Other values (960)7823
97.9%
2025-11-21T18:50:38.348847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5561
 
12.6%
r3027
 
6.9%
t2967
 
6.7%
o2872
 
6.5%
i2813
 
6.4%
a2771
 
6.3%
n2618
 
5.9%
l1863
 
4.2%
s1560
 
3.5%
c1385
 
3.1%
Other values (40)16691
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)44128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5561
 
12.6%
r3027
 
6.9%
t2967
 
6.7%
o2872
 
6.5%
i2813
 
6.4%
a2771
 
6.3%
n2618
 
5.9%
l1863
 
4.2%
s1560
 
3.5%
c1385
 
3.1%
Other values (40)16691
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5561
 
12.6%
r3027
 
6.9%
t2967
 
6.7%
o2872
 
6.5%
i2813
 
6.4%
a2771
 
6.3%
n2618
 
5.9%
l1863
 
4.2%
s1560
 
3.5%
c1385
 
3.1%
Other values (40)16691
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5561
 
12.6%
r3027
 
6.9%
t2967
 
6.7%
o2872
 
6.5%
i2813
 
6.4%
a2771
 
6.3%
n2618
 
5.9%
l1863
 
4.2%
s1560
 
3.5%
c1385
 
3.1%
Other values (40)16691
37.8%
Distinct968
Distinct (%)12.1%
Missing10
Missing (%)0.1%
Memory size62.6 KiB
2025-11-21T18:50:38.622088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.496245307
Min length1

Characters and Unicode

Total characters43915
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowExpect
2nd rowPopulation
3rd rowCompare
4th rowAgreement
5th rowPartner
ValueCountFrequency (%)
hold19
 
0.2%
condition18
 
0.2%
half17
 
0.2%
language17
 
0.2%
reflect16
 
0.2%
news16
 
0.2%
call16
 
0.2%
check16
 
0.2%
marriage16
 
0.2%
value15
 
0.2%
Other values (958)7824
97.9%
2025-11-21T18:50:38.977136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5461
 
12.4%
r2959
 
6.7%
t2897
 
6.6%
o2884
 
6.6%
a2786
 
6.3%
i2776
 
6.3%
n2591
 
5.9%
l1972
 
4.5%
s1496
 
3.4%
u1340
 
3.1%
Other values (40)16753
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)43915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5461
 
12.4%
r2959
 
6.7%
t2897
 
6.6%
o2884
 
6.6%
a2786
 
6.3%
i2776
 
6.3%
n2591
 
5.9%
l1972
 
4.5%
s1496
 
3.4%
u1340
 
3.1%
Other values (40)16753
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)43915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5461
 
12.4%
r2959
 
6.7%
t2897
 
6.6%
o2884
 
6.6%
a2786
 
6.3%
i2776
 
6.3%
n2591
 
5.9%
l1972
 
4.5%
s1496
 
3.4%
u1340
 
3.1%
Other values (40)16753
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)43915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5461
 
12.4%
r2959
 
6.7%
t2897
 
6.6%
o2884
 
6.6%
a2786
 
6.3%
i2776
 
6.3%
n2591
 
5.9%
l1972
 
4.5%
s1496
 
3.4%
u1340
 
3.1%
Other values (40)16753
38.1%

local_demand_index_2025
Real number (ℝ)

Distinct7960
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5016.497197
Minimum1.14
Maximum9999.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-21T18:50:39.079410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.14
5-th percentile505.1595
Q12474.475
median5062.695
Q37516.615
95-th percentile9500.916
Maximum9999.53
Range9998.39
Interquartile range (IQR)5042.14

Descriptive statistics

Standard deviation2895.629244
Coefficient of variation (CV)0.5772213419
Kurtosis-1.209340586
Mean5016.497197
Median Absolute Deviation (MAD)2529.705
Skewness-0.02247456296
Sum40131977.58
Variance8384668.72
MonotonicityNot monotonic
2025-11-21T18:50:39.184063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
923.983
 
< 0.1%
6741.892
 
< 0.1%
1599.282
 
< 0.1%
2667.522
 
< 0.1%
2687.212
 
< 0.1%
5848.892
 
< 0.1%
4574.052
 
< 0.1%
4980.682
 
< 0.1%
8275.382
 
< 0.1%
5779.542
 
< 0.1%
Other values (7950)7979
99.7%
ValueCountFrequency (%)
1.141
< 0.1%
3.51
< 0.1%
3.951
< 0.1%
7.111
< 0.1%
7.451
< 0.1%
ValueCountFrequency (%)
9999.531
< 0.1%
9998.461
< 0.1%
9998.311
< 0.1%
9997.131
< 0.1%
9995.611
< 0.1%

price_per_night_inr
Real number (ℝ)

Distinct7973
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4983.951836
Minimum9.26
Maximum9999.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-21T18:50:39.284178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.26
5-th percentile510.9705
Q12474.3225
median4969.45
Q37475.5175
95-th percentile9464.3275
Maximum9999.11
Range9989.85
Interquartile range (IQR)5001.195

Descriptive statistics

Standard deviation2878.231772
Coefficient of variation (CV)0.5774999171
Kurtosis-1.204041184
Mean4983.951836
Median Absolute Deviation (MAD)2500.2
Skewness0.01273441055
Sum39871614.69
Variance8284218.134
MonotonicityNot monotonic
2025-11-21T18:50:39.395463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3640.142
 
< 0.1%
2483.752
 
< 0.1%
3101.942
 
< 0.1%
4221.962
 
< 0.1%
7381.962
 
< 0.1%
6566.122
 
< 0.1%
2994.882
 
< 0.1%
8739.422
 
< 0.1%
2554.372
 
< 0.1%
8867.162
 
< 0.1%
Other values (7963)7980
99.8%
ValueCountFrequency (%)
9.261
< 0.1%
101
< 0.1%
10.071
< 0.1%
12.661
< 0.1%
13.41
< 0.1%
ValueCountFrequency (%)
9999.111
< 0.1%
9998.11
< 0.1%
99981
< 0.1%
9997.341
< 0.1%
9996.911
< 0.1%